Distinguishing the target from the background, judging target occlusion, and real-time processing are the problems that the visual tracking algorithm still needs to solve. Color information and position information of the target block are fused as new features to track the target under the framework of particle filtering. First, the hues, saturation, value space, and color integral graph of the image are constructed. The vector representation of the target is obtained on the color integral image by sparse matrix. Then, candidate particles are produced by a particle filter and the sampling mode of particles is adjusted by a uniform acceleration model. The difference of particles reflects the position and scale change of the target. Finally, the candidate with the smallest eigenvector projection error is taken as the tracking target and the feature template is updated based on the tracking results. The presented algorithm can be used to track a single target in the color image sequence and has some robustness to the scale change, occlusion, and morphological change of the target. Experiment results on public datasets show that the proposed algorithm performs favorably in both speed and tracking effect when compared with other conventional trackers.
In the pulse laser ranging system based on time-of-flight measurement, since different targets have different reflection characteristics, the echo light intensity will affect the leading edge moment received by the range finder, which results in the deviation of the ranging result. In order to address this problem, this paper proposes a leading edge time correction model based on pulse width. The pulse width of the echo is positively correlated with the light intensity, so the pulse intensity can be used to characterize the light intensity and correct the leading edge time. According to Marius law, the leading edge moment acquisition experiments are carried out under different echo intensities produced by polarization state generator (PSG). It has been demonstrated that the presented model is consistent with experimental data. From the analysis and discussion, it is shown that the correction model can effectively correct the error caused by the echo light intensity of the pulsed laser ranging system, thus improving the accuracy of ranging.
Camera’s internal parameter calibration is an important problem in computer vision. The moment, a precise targets like checkboard is required when camera calibration is performed. But this method is not suitable for a telephoto lens. In order to overcome this problem, a new camera calibration model is constructed by installing the debugged telephoto camera on an accurate two-dimensional rotating platform. Let the camera move around the rotating axis of the rotating platform. The camera takes pictures of distant objects directly. Then, the next picture is get by rotating the two-dimensional platform. The image coordinates , of the same point in space, in different images are obtained by image matching. The motion matrix of the camera is calculated from the readings of the two-dimensional rotating platform. Last, the optimization equation can be enumerated to solve the internal parameters. The experimental results show that the internal parameters obtained by the algorithm can satisfy the calculation accuracy.
Nowadays, with the boosting incomes and rapid development of science, car has become one of the most important transport vehicle. Driving environment and safety has attracted much attention in the automotive design field. Therefore, it is extremely urgent to develop the intelligent and reliable safety technologies such as vehicle active collision warning system. There are lots of studies focused on the critical research of machine vision ranging technology, however, the installation error of binocular ranging may result in inaccurate measurement accuracy. In this study, we use an improved monocular ranging to measure distance. The traditional monocular ranging models based on the principle of pinhole imaging, static image ranging model and etc. Most of the models require specific prior information of the vehicle, the applicable conditions of the model may be too idealistic and not applicable to general situations. In order to solve the contradiction, our research proposes a creative monocular ranging model to measure the distance between two vehicles. The model is based on the camera space projection relationship taking the factors of the camera's pitch angle into account. Our model has universal application significance using the simple implementation method with residual method. Based on the model, we amended the camera's pitch angle after the experiments. Meanwhile, the accuracy of the model is guaranteed by analyzing the factors affecting the accuracy of the range. The experimental results show that the error is controlled within 10%, which can meet the accuracy requirements of the system.
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